Open-world navigation requires robots to make decisions in complex everyday environments while adapting to flexible task requirements. Conventional navigation approaches often rely on dense 3D reconstruction and hand-crafted goal metrics, which limits their generalization across tasks and environments. Recent advances in vision–language navigation (VLN) and vision–language–action (VLA) models enable end-to-end policies conditioned on natural language, but typically require interactive training, large-scale data collection, or task-specific fine-tuning with a mobile agent. We formulate navigation as a sparse subgoal identification and reaching problem and observe that providing visual anchoring targets for high-level semantic priors enables highly efficient goal-conditioned navigation. Based on this insight, we select navigation frontiers as semantic anchors and propose OpenFrontier, a training-free navigation framework that seamlessly integrates diverse vision–language prior models. OpenFrontier enables efficient navigation with a lightweight system design, without dense 3D mapping, policy training, or model fine-tuning. We evaluate OpenFrontier across multiple navigation benchmarks and demonstrate strong zero-shot performance, as well as effective real-world deployment on a mobile robot.

OpenFrontier detects and semantically evaluates visual frontiers directly in image space, enabling flexible language-conditioned goal reasoning without 3D reconstruction. The detected frontiers are grounded into the 3D metric space to drive long-horizon, natural-language-conditioned navigation across diverse goals in a fully zero-shot manner — without training or fine-tuning. By using frontiers as semantic anchors, OpenFrontier seamlessly integrates diverse vision–language prior models within a lightweight system, sidestepping the need for dense 3D mapping, policy training, or model fine-tuning.